Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Graph Matching via Multiplicative Update Algorithm
Authors: Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world matching tasks demonstrate the effectiveness and benefits of the proposed MPGM algorithm. We have applied MPGM algorithm to several matching tasks. Our method has been compared with some other state-of-the-art methods including SM [15], IPFP [16], SMAC [5], RRWM [3] and FGM [24]. |
| Researcher Affiliation | Academia | Bo Jiang School of Computer Science and Technology Anhui University, China EMAIL Jin Tang School of Computer Science and Technology Anhui University, China EMAIL Chris Ding CSE Department, University of Texas at Arlington, Arlington, USA EMAIL Yihong Gong School of Electronic and Information Engineering Xi an Jiaotong University, China EMAIL Bin Luo School of Computer Science and Technology, Anhui University, China EMAIL |
| Pseudocode | No | The paper describes the update rule via equations and text, but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | In this section, we perform feature matching on CMU and YORK house sequences [3, 2, 18]. We evaluate our MPGM on the dataset [17] whose images are selected from Pascal 2007 3. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The affinity matrix W has been computed as Wij,kl = exp( rik r jl 2 F /0.0015) and In experiments, we initialize our MPGM with uniform solution and obtain similar results when initializing with SM solution. |